Improving Auditory-Perceptual Voice Assessment: A Hybrid In-Class and Online CAPE-V Training Study
Bibliographic record
Abstract
BACKGROUND: Auditory-perceptual training is essential to reduce rating variability, yet most studies on hybrid training approaches are scarce, and most emphasize reliability rather than accuracy relative to expert standards. OBJECTIVE: This study evaluated a hybrid teaching tool combining in-class discussion with optional online practice (All-Voiced) for improving accuracy, reliability, and generalization of consensus auditory-perceptual evaluation of voice (CAPE-V) ratings in speech-language pathology students on both vowels and sentences. METHODS: Sixty-six graduate students completed a five-step CAPE-V training sequence, including baseline ratings, guided in-class discussion of prototypical samples, repeated ratings, optional online training, and a final test. Students rated Overall Severity, Roughness, and Breathiness relative to expert anchors. Accuracy (mean absolute error, % within 10 mm, and interquartile range [IQR]) and reliability (intraclass correlation coefficients) were analyzed for step, discussion, training, novelty, and sample type effects. RESULTS: Learners improved significantly after in-class discussion (P < 0.001), with gains largely retained at final testing. Breathiness showed the largest improvement, approaching clinical minimally detectable change thresholds. Optional online training yielded modest additional benefits, significant only for Breathiness (P < 0.001) and for Overall Severity mean absolute error (P = 0.03). Performance generalized less effectively to novel than familiar voices (P ≤ 0.002), except for Overall Severity % within IQR (P = 0.47). Across Step, Discussion, Training, and Novelty conditions, sentences were generally rated more accurately than vowels: they showed consistently lower errors (mean absolute error, P ≤ 0.014) and higher accuracy within 10 mm (P ≤ 0.05), while % within IQR advantages emerged mainly for Roughness (Step and Discussion, P < 0.001) and Breathiness (Training and Novelty, P ≤ 0.002), but not for Overall Severity. CONCLUSIONS: Hybrid training combining expert-led discussion with online practice improves both accuracy and reliability of CAPE-V ratings in novices. Even modest changes represent important pedagogical gains that support more consistent and valid clinical decision-making.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".